Linguistically-motivated Tree-based Probabilistic Phrase Alignment
Toshiaki Nakazawa

Sadao Kurohashi

Graduate School of Informatics, Kyoto University
Yoshida-honmachi, Sakyo-ku
Kyoto, 606-8501, Japannakazawa@nlp.kuee.kyoto-u.ac.jp kuro@i.kyoto-u.ac.jp

Abstract
In this paper, we propose a probabilisticphrase alignment model based on dependency
trees. This model is linguistically-motivated,using syntactic information during alignment
process. The main advantage of this model isthat the linguistic difference between source
and target languages is successfully absorbed.
It is composed of two models: Model1 is using
content word translation probability and function word translation probability; Model2 uses
dependency relation probability which is defined for a pair of positional relations on dependency trees. Relation probability acts as
tree-based phrase reordering model. Since thismodel is directed, we combine two alignment
results from bi-directional training by symmetrization heuristics to get definitive alignment. We conduct experiments on a JapaneseEnglish corpus, and achieve reasonably highquality of alignment compared with wordbased alignment model.

1

Introduction

Most of statistical machine translation (SMT) systems are based on “word-based” alignment method
starting with IBM models (Brown et al., 1993).
Based on the word alignment results, some enhanced and successful models which extract phrases
have been proposed and established the state-of-theart Phrase-Based SMT models (Koehn et al., 2003).
Another approaches incorporate syntactic information by parsing source or target sentences (Quirk etal., 2005; Galley et al., 2006; Cowan et al., 2006).
Chiang (2005) proposed hierarchical phrase-basedtranslation model which was based on a weighted
synchronous CFG. This model could handle more
complex linguistic phenomena which sequences ofwords could not do. However, all of these models are on the basis of IBM models, which do notessentially consider syntactic information. Wordbased alignment method works well for language
pairs whose linguistic structure is not so different
from each other (such as English v.s. European
languages), but not for language pairs with great
difference in linguistic structure (such as JapaneseEnglish). For a linguistically different pairs, deeper
natural language processing (NLP) analysis is necessary even during alignment process.
Watanabe et al. (2000) and Menezes and Richardson (2001) proposed a structural alignment methods. These methods use heuristic rules when resolving correspondence ambiguities, not considering the consistency between two dependency structure as a whole. Yamada and Knight (2001) and
Gildea (2003) proposed a tree-based probabilisticalignment methods. These methods reorder, insert
or delete sub-trees of one side to reproduce the otherside. The constraints of using syntactic information
is often too rigid. Yamada and Knight flattened thetrees by collapsing nodes, Gildea cloned the subtrees to deal with the problem.
Our method proposed in this paper does not require any operations for controlling tree structures,
just align phrase-to-phrase on dependency structure.
Though our model is more simple than well-known
IBM Model3 or greater, our model can achieve highaccuracy of alignment and high quality of translation. We propose a probabilistic tree-based phrase

alignment model. Since it uses dependency structure, our method can overcome the difference of languages even if they are structurally different fromeach other, which simple statistical word alignment
models are not able to. It can be said as a tree-based
phrase reordering model.
In section 2, our proposed model is illustrated in a
general way, and in the following section we explain
the model in detail using simple examples. The symmetrization algorithm is shown in section 4. We performed some experiments to evaluate our proposal,
which are reported in section 5. Finally, we give ashort conclusion and future work.

2

Tree-based Probabilistic Phrase
Alignment Model

We suppose Japanese for source language and English for target language in the description of ourmodel. Note that the model is not specialized forthis language pair, it can be applied to any language
pairs.

f5
f2(propylene)(glycol)

(wa)

䜾䝹䝁䞊䝇

f9
f7
f4
f1

e6
e2

increases⾑ ୰(blood) (inside)f8

f3

glycol䜾䝸䝁䞊䝹 ࡣf10

f6

Propylene

䝥䝻䝢䝺䞁

e3

Pair 1blood

e11(glucose)

glucose and e10䜲䞁䝇䝸䞁 ࢆ(insuline)(wo)in insulinୖ᪼ ࡉ ࡏ

(increase) (sase:causa!ve)

and

⾑ ୰

(blood) (inside)decreases

䠪䠡䠢䠝
⃰ᗘ ࢆ

Pair 2(wo)(concentra!on)ῶᑡ ࡉ ࡏࡿ

(decrease) (saseru:causa!ve)

e7
e4
e1

NEFA

e8

in concentra!one5

in the bloode9

Figure 1: Example of dependency trees.

Figure 1 shows an example of dependency structure. Each node of the tree corresponds to a linguistic phrase. Underlined words are handled as function
words, others are content words. Our model uses the
linguistic phrase as an unit of alignment rather thana word. The root of a tree is placed at the extreme
left and phrases are placed from top to bottom.
2.2 Tree-based Model

2.1 Dependency Analysis of Sentences
Since our model utilizes dependency tree structures, both source and target sentences are parsed
at first. Japanese sentences are converted into dependency structures using the morphological analyzer JUMAN (Kurohashi et al., 1994), and thedependency analyzer KNP (Kurohashi and Nagao,1994). Japanese dependency structure consists ofnodes which correspond to content words. Function
words such as post-positions, affixes, and auxiliary
verbs are included in the nodes.
For English sentences, Charniak’s nlparser is used
to convert them into phrase structures (Charniak
and Johnson, 2005), and then they are transformed
into dependency structures by handmade rules defining head words for phrases. As is the case withJapanese, each node in this dependency tree consists of a content word and related function words1 .
We define function words as the words with tagsof “IN”(preposition or subordinating conjunction),“TO”, “MD”(modal), “CC”(coordinating conjunction) by nlparser.
1

There would be some special cases that a phrase has nocontent word. See a phrase “and” in figure 1. Also, there would
be a phrase which has more than one content words.

In IBM models (Brown et al., 1993), the best alignment â between given source (French) sentence f
and target (English) sentence e is acquired by thefollowing equation:â = argmax p(f |e, a) · p(a|e)(1)

awhere p(f |e, a) is called as “lexicon probability”
and p(a|e) is called as “alignment probability”.
Since our model is based on dependency tree
structure, we can find the best alignment â between
given source (Japanese) tree Tf and target (English)
tree Te as follows (while focusing on Japanese andEnglish, we use the common notation for indexes asf and e):â = argmax p(Tf |Te , a) · p(a|Te )(2)aSuppose Tf consists of J nodes (equivalent tophrase) f1 , ..., fJ , Te consists of I nodes e1 , ..., eI .
In IBM models, f1 represents the first word of thesentence and fJ represents the last word. On theother hand, since we are handling dependency tree
structures, f1 represents the root node of the tree
and fJ represents one of the leaves of the tree inour model. The parent node is denoted with minus(-) mark on its index, that is, the parent node of fj
is fj− . With these notations, we decompose lexicon
probability and alignment probability as follows:p(Tf |Te , a) ≈J∏p(fj |eaj )(3)

EM algorithm is adopted for the parameter estimation, and best alignment â will be found. We
train the model bi-directionally and acquire twobest alignment results. These results are combined
into one definitive alignment using symmetrization
heuristics.

j=1

p(a|Te ) ≈J∏

3 Model Trainingp(rel(eaj , eaj− )|rel(fj , fj− )) (4)j=1

aj denotes the phrase number which source
phrase fj corresponds to, then eaj denotes a target
side phrase which corresponds to source phrase fj .
p(fj |eaj ) is a phrase translation probability. Words
in a phrase are categorized into two groups: content words or function words. We define the phrasetranslation probability as a product of content wordtranslation probability and function word translation
probability.
p(fj |eaj ) = pcont. (fj |eaj ) · pf unc. (fj |eaj )(5)Here, two or more content or function words in one
phrase are considered together. In figure 1, twoJapanese function words “さ” and “せ” are combined and considered as one word “させ”.
rel(fj , fj− ) represents the dependency relation
between fj and fj− on dependency tree. In caseno corresponding candidate is found for fj− , we refer the parent node of fj− , and this is repeated until
a node with corresponding candidate is found, thenthe relation between this node and fj is considered.
Take Pair 1 in figure1 for example. f10 is a childof its parent f8 , and corresponding English node e11
is also a child of its parent e10 . For Pair 2 as ananother example, the relation in source side is same
to Pair 1 (child), as for target side, e9 is a child of e5 ,parentchild
which is a parent of e8 (e8 −−−→ e5 −−→ e9 ).
p(rel(eaj , eaj− )|rel(fj , fj− )) represents the dependency relation probability, which is assigned to apair of source and target side relations between two
phrases. This can be said a tree-based reorderingmodel.
The unknown parameters θ are determined by
maximizing the likelihood on the parallel training
corpus which consists of S parallel sentences:θ̂ = argmax
θ

S ∑
∏
s=1 a

p(Tf |Te , a) · p(a|Te )(6)

Our proposed tree-based phrase alignment model iscomposed of three probabilities, content word translation probability, function word translation probability, and dependency relation probability as shownin previous section. The content / function wordtranslation probabilities are independent from otherphrases than the focusing phrase. Dependency relation probability depends on which phrase the parent
phrase is correspond to in the target tree.
In a first step, the model learning on two translation probabilities only (Model1) is conducted. Next,
the enhanced model including dependency relation
probability (Model2) is learned with the parameter values learned in Model1 as an initial parameters. Model1 can be efficiently learned without approximation like IBM model1 and 2. For Model2,
some approximation is necessary as IBM model3 or
greater. Here, we adopt beam-search algorithm.
3.1 Model1
Each phrase in source side fj (1 ≤ j ≤ J) can
correspond to an arbitrary phrase in target side ei(1 ≤ i ≤ I) or a NULL phrase (e0 ), if fj does
not correspond to any phrase, independently of othersource phrase. A probability of one possible alignment p(a, Tf |Te ) is calculated as follows:p(a, Tf |Te ) =J∏pcont. (fj |eaj ) · pf unc. (fj |eaj )

j=1

(7)
Also, p(Tf |Te ) is calculated as:p(Tf |Te ) =∑p(a, Tf |Te )(8)aSince there are (I + 1)J possible alignments, wehave to do 2J ∗ (I + 1)J arithmetic operations toevaluate this expression. However, the expression

f3

above can be transformed like:
J
∑∏
a j=1

p(fj |eaj ) =

J ∑
l
∏

p(fj |eaj )

(9)

e2

f2

j=1 i=0This last expression only requires a quadratic number of arithmetic operations to evaluate, therefore no
approximation is needed.
As initial parameters, we use uniform probabilities.
3.2 Model2
In the first iteration of Model2, dependency relation
probabilities are set to be uniform, and learning result of translation probabilities in Model1 is used as
the initial parameters. Dependency relation probabilities are calculated according to the equation 4.
It is impossible to enumerate all the possible alignment, we consider only a subset of “good-looking”alignments using beam-search algorithm.
Dependency relation probability refers to the relation between a source side phrase fj and its parent phrase fj− (rel(fj , fj− )), and the relation between corresponding target side phrases eaj andeaj− (rel(eaj , eaj− )). fj− represents the nearest parent phrase which is not aligned to NULL. Dependency relation (rel(P1 , P2 )) indicates a path to aphrase (P2 ) to another one (P1 ). We use the notations below.
• “c-” if P1 is a pre-child of P2• “c+” if P1 is a post-child of P2• “p+” if P2 is a pre-child of P1• “p-” if P2 is a post-child of P1• “INCL” if P1 and P2 are same phrase
Relations between two phrases which are far morethan 1 node from each other are expressed by puttingthese marks.
A head phrase of a sentence is supposed todepends on the imaginary root node which is a
start point of beam-search. It corresponds to the
SOS (start-of-sentence) in word-base models. Figure 2 is an abstract example where beam width =5. f1 and e1 which are the head phrases depend
on imaginary root node. First, we focus on f1
which has three correspondence candidates, e1 , e2
and NULL (e0 ). Alignment probabilities are calculated as p(f1 |e1 ) · p(ROOT|ROOT), p(f1 |e2 ) ·e1

f1

Figure 2: Abstraction of alignment.

p(ROOT|ROOT;c), p(f1 |NULL) · p(ROOT|NULL)
respectively. Here, “ROOT” means that the phrase
is a child of imaginary root node.
As for f2 in the next place, there are also three correspondence candidates. Consequently, there should
be nine alignment candidates in total (table 1 showseach probability). These nine are sorted by the probabilities and only 5-best alignments are preserved.
When we consider about f3 , we take the five alignment candidates into account, and this results in generating 15 (5*3) alignment candidates, again we discard except top 5 probable alignments. These steps
are repeated until all the source side phrases arealigned to any one target side phrase. Parameters
are updated using only the last 5-best alignments.

4 Symmetrization Algorithm
After the model learning is finished in each direction, two alignment results can be acquired. With
these results, we generate final alignment by combining two alignment results using heuristic rules
like Koehn et al. (2003). The differences between
theirs and ours are: 1) alignment unit is phrase rather
than word, 2) using tree structure during growing
process, 3) n-best alignment results in each direction
are considered.
Reduplication N-best alignment results acquiredin each model learning, 2n alignments in total are
reduplicated, then possible alignment points arescored from 1 to 2n (see figure 3 where n=5). In
descending order of the score, possible alignment
points are adopted as definitive alignment points if
there is no point with higher score than the focusing
point in the same row or column. In the example,
dark colored six points are adopted. If we choose
n=1, this algorithm is same to simple “intersection”.

f1
e1
e1
e1
e2
e2
e2
NULL
NULL
NULL

f2
e1
e2
NULL
e1
e2
NULL
e1
e2
NULL

Table 1: Probability calculation example.
Probabilityp(f1 |e1 ) · p(ROOT|ROOT) · p(f2 |e1 ) · p(INCL|c-)p(f1 |e1 ) · p(ROOT|ROOT) · p(f2 |e2 ) · p(c-|c-)p(f1 |e1 ) · p(ROOT|ROOT) · p(f2 |NULL) · p(NULL|c-)p(f1 |e2 ) · p(ROOT;c-|ROOT) · p(f2 |e1 ) · p(p+|c-)p(f1 |e2 ) · p(ROOT;c-|ROOT) · p(f2 |e2 ) · p(INCL|c-)p(f1 |e2 ) · p(ROOT;c-|ROOT) · p(f2 |NULL) · p(NULL|c-)p(f1 |NULL) · p(NULL|ROOT) · p(f2 |e1 ) · p(ROOT|ROOT;c-)p(f1 |NULL) · p(NULL|ROOT) · p(f2 |e2 ) · p(ROOT;c-|ROOT;c-)p(f1 |NULL) · p(NULL|ROOT) · p(f2 |NULL) · p(NULL|ROOT;c-)

Growing Possible points around the definitive
points are adopted. Here, “around” does not meanright before or after the phrases, it means the continuous phrases on the dependency trees. Even if aright before or after the definitively aligned phrase
is possible point, it would not be adopted when they
are not directly connected. In the example, there occurs three growing alignments colored in gray indicated with thick arrows.
Handling Isolation Not adopted possible points
which are not aligned to any phrase in both source
and target language are adopted. Left bottom isolated part (gray colored points) in the example isadopted by this process.

5

Experiments and Results

23

http://www.jst.go.jp/
http://www.nict.go.jp/

ؒ‫ؤ‬
ؒ ‫ؤ‬
ؒ ‫ؤ‬
‫ؤ‬
‫ج‬
‫ؤ‬
‫ج‬
‫ؤ‬

We conducted both alignment and translation evaluation experiments. JST 2 Japanese-English paper
abstract corpus consists of 1M parallel sentenceswere used for model training. This corpus was constructed from 2M Japanese-English paper abstract
corpus belongs to JST by NICT 3 using the methodof Uchiyama and Isahara (2007).
We made gold-standard alignment for 100 sentence pairs among the 1M parallel sentences byhand. The annotations are only sure (S) alignments (no possible (P ) alignments (Och and Ney,
2003)). The unit of evaluation was morpheme-basefor Japanese and word-base for English.
We used precision, recall, and F-measure as evaluation criterion. The evaluation results are shown intable 2. We used “3-best-grow” and “5-best-grow”

5 2‫ ؜‬invesgaon10ؒ‫ ؤ‬of the hint10
ؒ ‫ ؤ‬for challenge10ؒ‫ ؤ‬for future
was made5 3
5‫ ؤ‬through review
1 5‫ ؤ‬of the history73ؒ‫ ؜‬technology and10ؒ‫ ج‬typical59‫ ؤ‬of invenons
7ؒ‫ ؜‬tweneth‫ ؤ‬in the century 7 1

Figure 3: Example of growing the alignment points.

symmetrization heuristics which uses 3-best and 5best alignment results respectively from each direction and extend the alignment points using heuristic
rules explained in the previous section.
For comparison, we segmented the data using themorphological analyzer JUMAN (Kurohashi et al.,1994) for Japanese sentences and created alignments
using freely available word alignment tool GIZA++(Och and Ney, 2003). We conducted word alignment bidirectionally with its default parameters andmerged them using seven types of symmetrization
heuristics (Koehn et al., 2003) shown in table 2.
Training are run on original forms of words for bothproposed model and GIZA++.
For translation evaluation, we use 500 paper abstract sentences which are parts of JST corpus. Notethat test sentences are not included in training corpus. As a decoder, we used state-of-the-art phrasebased SMT toolkit Moses (Koehn et al., 2007) withits default options except for phrase table limit (20→ 10) and distortion limit (6 → -1 means infinite).
Evaluation was done with all the punctuations being
deleted and case-insensitively. The BLEU scores ofeach alignment methods are shown in table 2, in thelast column.
Actually, it is hard to integrate proposed alignment results into Moses decoder because our modelis based on “linguistic phrase”. If we align all words
to all words in a corresponding two phrases, Moses
would fail to translate a content word with different function words from the learned phrase pair. To
avoid this problem to some extent, we aligned content words to content words, and function words tofunction words separately, in addition, no article in
English sentences was aligned. Of course this is not
sufficient at all, Japanese has many kinds of function
words which English does not have. Even in translation process, it is necessary to handle the function
words carefully.

6

Discussions

Table 2 shows that our proposed model could
achieve reasonably high accuracy of alignment, and
it is better than word-base models. As an example, word-base alignment result in figure 4 fails tofind the correspondence between content words “上昇” and “increase”, what is worse, “increase” isTable 2: Experimental results of alignment and translation.

Proposed
1-best1-best-grow
3-best-grow
5-best-grow
GIZA++
intersection
grow
grow-finalgrow-final-and
grow-diaggrow-diag-finalgrow-diag-final-and

Pre

Rec

F

BLEU

90.92
83.30
81.21
80.59

41.69
54.33
56.52
57.33

57.17
65.76
66.65
67.00

12.73
14.97
15.09
15.40

88.14
83.50
67.19
78.00
77.34
67.24
74.95

40.18
49.65
56.91
52.93
53.18
56.63
54.26

55.20
62.27
61.63
63.06
63.03
61.48
62.95

16.35
17.05
17.85
17.70
17.89
17.80
17.76

incorrectly aligned to a word “は (wa)” which is afunction word. This is because, as mentioned in
section 1, the statistical methods work well for language pairs that are not so different regarding language structure. Japanese and English have significantly different structure: Japanese sentences consist of SOV word order, but English word order is
SVO. For such language pair as Japanese and English, deeper sentence analysis using NLP resources
is necessary and useful, like in our method. By using the tree structure in figure 5, these two words(phrases) are correctly aligned.
Even if the alignment accuracy was improved,
this did not lead to improve the translation quality
referring to the BLEU score, BLEU score of our proposed model is worse than that of word-base models.
One reason of this is that, as mentioned above, theinfelicity of integrating our alignment results into
Moses decoder. Another reason is that BLEU is essentially insensitive to syntactic structure. The translation result may indeed better from the point of dependency structure. We need to try parsing base line
output and the output of the realigned system andsee if the parsing results improve.
Some of recent studies suggest that there is lessrelationship between alignment quality and translation results (Lopez and Resnik, 2006; Ayan andDorr, 2006). Even if the contribution to translation quality is small, there is no doubt that betteralignment quality leads to better translation, whichPropylene
䕔glycol䕔
increases
䕔
in
䕔䕔
blood
䕔
glucose䕔
andinsulin䕔
anddecreasesin
NEFA
concentra!on
in
the
blood

䕔

‫ ؜‬Propylene䕔‫ ؜‬glycol䕔‫ ج‬increases䕔ؒ ؒ‫ ؜‬blood䕔ؒ ؒ ‫ ؜‬glucose and䕔ؒ ‫ ؤ‬in insulin䕔‫ ج‬and䕔
decreases䕔ؒ ‫ ؜‬NEFA䕔‫ ؤ‬in concentra!on䕔‫ ؤ‬in the blood
䕔

䕔
䕔
䕔
䕔
䕔䕔

‫ج‬
ؒ‫ؤ‬
ؒ ‫ؤ‬
‫ج‬
ؒ‫ؤ‬
ؒ ‫ؤ‬
ؒ
‫ؤ‬
‫ؤ‬
‫ؤ‬

Figure 4: Result of word-base alignment (grow-diagfinal-and).

is more prominence in case only small amount ofparallel corpus can be used. What is more, mostof these works are done on similar language pairs,such as English v.s. Chinese, French. We are now
investigating the contribution of alignment quality improvement to translation quality in JapaneseEnglish, language pair whose structure is very different.
According to the study about alignment quality(Fraser and Marcu, 2007), if the gold standard data
only contains sure alignments, precision is much important than recall. Modified F-measure calculatedby the equation 10 shows higher correlation to translation quality if α is set to be more than 0.5 (meansemphasizing on precision).
Modified F-measure =

1
(1−α)
α
Precision + Recall(10)

However, our results disagree with this argument.
There seems no relation between precision andtranslation quality, indeed, F-measure has good correlation with BLEU score.

7

Related Work

Our proposed model is similar to the work of Cherry
and Lin (2003). They use dependency tree structure
for source side and construct a probabilistic model.

Figure 5: Result of phrase-base alignment.

The differences between their model and ours arefollowing.
• Unit of alignment: Their model aligns words
to words. Ours aligns syntactic phrases tophrases.
• Parser: They use a parser for source side only,
and reproduce the target side dependency structure introducing a cohesion constraint. We use
parsers for both source and target side, and do
not require any constraints.
• Alignment constraint: They can only makeone-to-one links. We can make even many-tomany links.
The third point is the most crucial for alignment,
because there often occurs one-to-many or manyto-many correspondences. Their model was tested
on English-French corpus, which are very similar
languages, therefore even one-to-one model could
achieve high accuracy.

8 Conclusion
In this paper, we have proposed a linguisticallymotivated probabilistic phrase alignment modelbased on dependency tree structure. Experimental
results show that word-based statistical alignment
model does not work well for linguistically different language pair, and it can be resolved by using
syntactic information.
We have conducted the experiments only on
Japanese-English corpus. To support firmly ourallegation that syntactic information is important,
it is necessary to do more investigation on otherlanguage pairs. We are mounting an experimenton Japanese-Chinese scientific paper corpus, whosecharacteristics are similar to the Japanese-English
corpus we used in the experiment.
Proposed model handles content words and function words separately. This is harmful because function words in one side may appear as content words
in the other side, and content words also may become function words. We need to construct more
flexible model to solve this problem.
Moreover, one phrase often corresponds to morethan one phrases in the other side. Currently weare handling such correspondence by symmetrization heuristics (growing). We are now trying to construct the framework which can model multiple ofphrases. This enhanced model may be able to learn
many-to-many alignment as one of the features ofthe model.
Most frequent alignment errors are derived fromparsing errors. Since our method highly depends
on the structural information, parsing errors easily make the alignment accuracy worse. Although
the parsing accuracy is basically high for Japanese(around 90% for newspaper sentences), it sometimes outputs wrong dependency structure because
there often appears technical, or unknown words
in the scientific paper, and this is same to English.
This problem is possible to be resolved by introducing parsing probabilities into our model as â =argmax p(Tf |Te , a) · p(a|Te ) · p(Tf ) · p(Te ) usingparsing tools (KNP and nlparser) which can output
n-best parsing with their parsing probabilities.

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